{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting Bi-LSTM Sentiment Classification Models With LIME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook loads the pretrained Bi-LSTM model following [PaddleNLP TextClassification](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification/rnn) and performs sentiment analysis on reviews data. The full official PaddlePaddle sentiment classification tutorial can be found [here](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification). \n",
    "\n",
    "Interpretations of the predictions are generated and visualized using LIME algorithm, specifically the `LIMENLPInterpreter` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "import jieba\n",
    "import interpretdl as it\n",
    "from interpretdl.data_processor.visualizer import VisualizationTextRecord, visualize_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have't done so, please first download the word dictionary that maps each word to an id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# downloads the word dict to assets/\n",
    "!wget https://paddlenlp.bj.bcebos.com/data/senta_word_dict.txt -P assets/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the word dict and specify the pretrained model path. Define the `unk_id` to be the word id for *\\[UNK\\]* token. Other possible choices include empty token *\\\"\\\"* and *\\[PAD\\]* token.\n",
    "\n",
    "To obtain the pretrained weights, please train a bilstm model following the [tutorial](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification/rnn) and specify the final `.pdparams` file position in `PARAMS_PATH` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_vocab(vocab_file):\n",
    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
    "    vocab = {}\n",
    "    with open(vocab_file, \"r\", encoding=\"utf-8\") as reader:\n",
    "        tokens = reader.readlines()\n",
    "    for index, token in enumerate(tokens):\n",
    "        token = token.rstrip(\"\\n\").split(\"\\t\")[0]\n",
    "        vocab[token] = index\n",
    "    return vocab\n",
    "\n",
    "PARAMS_PATH = \"assets/final.pdparams\"\n",
    "VOCAB_PATH = \"assets/senta_word_dict.txt\"\n",
    "\n",
    "vocab = load_vocab(VOCAB_PATH)\n",
    "unk_id = vocab['[UNK]']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the BiLSTM model using **paddlenlp.models** and load pretrained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddlenlp as ppnlp\n",
    "model = ppnlp.models.Senta(\n",
    "        network='bilstm',\n",
    "        vocab_size=len(vocab),\n",
    "        num_classes=2)\n",
    "\n",
    "state_dict = paddle.load(PARAMS_PATH)\n",
    "model.set_dict(state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a preprocessing function that takes in **a raw string** and outputs the model inputs that can be fed into paddle_model.\n",
    "\n",
    "In this case, the raw string is splitted and mapped to word ids. *texts* is a list of lists, where each list contains a sequence of padded word ids. *seq_lens* is a list that contains the sequence length of each unpadded word ids in *texts*. \n",
    "\n",
    "Since the input data is a single raw string. Both *texts* and *seq_lens* has length 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_fn(text):\n",
    "    texts = []\n",
    "    seq_lens = []\n",
    "\n",
    "    tokens = \" \".join(jieba.cut(text)).split(' ')\n",
    "    ids = []\n",
    "    unk_id = vocab.get('[UNK]', None)\n",
    "    for token in tokens:\n",
    "        wid = vocab.get(token, unk_id)\n",
    "        if wid:\n",
    "            ids.append(wid)\n",
    "    texts.append(ids)\n",
    "    seq_lens.append(len(ids))\n",
    "\n",
    "    pad_token_id = 0\n",
    "    max_seq_len = max(seq_lens)\n",
    "\n",
    "    texts = paddle.to_tensor(texts)\n",
    "    seq_lens = paddle.to_tensor(seq_lens)\n",
    "    return texts, seq_lens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the `LIMENLPInterpreter`. Define the reviews that we want to analyze. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lime = it.LIMENLPInterpreter(model)\n",
    "\n",
    "reviews = [\n",
    "    '这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般',\n",
    "    '作为老的四星酒店，房间依然很整洁，相当不错。机场接机服务很好，可以在车上办理入住手续，节省时间。'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, we iteratively `interpret` each review and grab weights for each token. For visualizasion purposes, word weights in each review are normalized to better illustrate differences between weights. Results for each review is stored in a list by making use of the `VisualizationTextRecord`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /tmp/jieba.cache\n",
      "Loading model cost 0.835 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table width: 100%><tr><th>True Label</th><th>Predicted Label (Prob)</th><th>Target Label</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>0 (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 这个                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 宾馆                        </font></mark><mark style=\"background-color: hsl(120, 75%, 84%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 比较                        </font></mark><mark style=\"background-color: hsl(0, 75%, 85%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 陈旧                        </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 了                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 特价                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 的                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 房间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 也                        </font></mark><mark style=\"background-color: hsl(120, 75%, 82%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(0, 75%, 85%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 一般                        </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark><mark style=\"background-color: hsl(120, 75%, 84%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 总体                        </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 来说                        </font></mark><mark style=\"background-color: hsl(0, 75%, 84%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 一般                        </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>1 (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 作为                        </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 老                        </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 的                        </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 四星                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 酒店                        </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 房间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 83%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 依然                        </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 整洁                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 87%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 相当                        </font></mark><mark style=\"background-color: hsl(120, 75%, 66%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 不错                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 机场                        </font></mark><mark style=\"background-color: hsl(0, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 接机                        </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 服务                        </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 好                        </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 可以                        </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 在                        </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 车上                        </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 办理                        </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 入住                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 手续                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 节省时间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark></td><tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_labels = [1, 0]\n",
    "recs = []\n",
    "\n",
    "for i, review in enumerate(reviews):\n",
    "    pred_class, pred_prob, lime_weights = lime.interpret(\n",
    "        review,\n",
    "        preprocess_fn,\n",
    "        num_samples=1000,\n",
    "        batch_size=10,\n",
    "        unk_id=unk_id,\n",
    "        pad_id=0,\n",
    "        return_pred=True)\n",
    "\n",
    "    words = \" \".join(jieba.cut(review)).split(' ')\n",
    "    interp_class = list(lime_weights.keys())[0]\n",
    "    word_importances = [t[1] for t in lime_weights[interp_class]]\n",
    "    word_importances = np.array(word_importances) / np.linalg.norm(\n",
    "        word_importances)\n",
    "    true_label = true_labels[i]\n",
    "    if interp_class == 0:\n",
    "        word_importances = -word_importances\n",
    "    rec = VisualizationTextRecord(words, word_importances, true_label,\n",
    "                                  pred_class[0], pred_prob[0],\n",
    "                                  interp_class)\n",
    "    recs.append(rec)\n",
    "\n",
    "visualize_text(recs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell's output is similar to the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='assets/lime_nlp_viz.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "firstEnv",
   "language": "python",
   "name": "firstenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
